Overview

Dataset statistics

Number of variables14
Number of observations7998
Missing cells18003
Missing cells (%)16.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory874.9 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric13

Variable descriptions

co_gtstuendlich gemittelte CO-Konzentration
pt08_s1_costuendlich gemittelte Sensorreaktion (nominell auf CO ausgerichtet) (Zinnoxid)
nmhc_gtstuendlich gemittelte Gesamtkonzentration an nicht-metanischem Kohlenwasserstoff
c6h6_gtstuendlich gemittelte Benzolkonzentration
pt08_s2_nmhcstuendlich gemittelte Sensorreaktion (nominell auf NMHC ausgerichtet) (Titandioxid)
nox_gtEchte stuendlich gemittelte NOx-Konzentration
pt08_s3_noxstuendlich gemitteltes Sensoransprechverhalten (nominell auf NOx ausgerichtet)
no2_gtstuendlich gemittelte NO2-Konzentration
pt08_s4_no2stuendlich gemittelte Sensorreaktion (nominell auf NO2 ausgerichtet) (Wolframoxid)
pt08_s5_o3stuendlich gemitteltes Sensoransprechverhalten (nominell O3-bezogen) (Indiumoxid)
tTemperatur
rhRelative Luftfeuchtigkeit
ahAbsolute Luftfeuchtigkeit
monthMonate der Erfassung
hourStunden der Erfassung

Alerts

date has a high cardinality: 7998 distinct values High cardinality
co_gt is highly correlated with pt08_s1_co and 8 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 8 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 9 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 8 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 8 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 8 other fieldsHigh correlation
t is highly correlated with nmhc_gt and 3 other fieldsHigh correlation
rh is highly correlated with tHigh correlation
ah is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
co_gt is highly correlated with pt08_s1_co and 8 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 8 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 8 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 8 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 8 other fieldsHigh correlation
t is highly correlated with pt08_s4_no2 and 2 other fieldsHigh correlation
rh is highly correlated with tHigh correlation
ah is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
co_gt is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 5 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 5 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 4 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 6 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 4 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with nmhc_gt and 1 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 7 other fieldsHigh correlation
co_gt is highly correlated with pt08_s1_co and 8 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 8 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 8 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 8 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with co_gt and 9 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 8 other fieldsHigh correlation
t is highly correlated with pt08_s4_no2 and 2 other fieldsHigh correlation
rh is highly correlated with tHigh correlation
ah is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
co_gt has 1839 (23.0%) missing values Missing
pt08_s1_co has 383 (4.8%) missing values Missing
nmhc_gt has 7139 (89.3%) missing values Missing
c6h6_gt has 492 (6.2%) missing values Missing
pt08_s2_nmhc has 2265 (28.3%) missing values Missing
nox_gt has 2032 (25.4%) missing values Missing
pt08_s3_nox has 503 (6.3%) missing values Missing
no2_gt has 1694 (21.2%) missing values Missing
pt08_s4_no2 has 414 (5.2%) missing values Missing
pt08_s5_o3 has 373 (4.7%) missing values Missing
t has 291 (3.6%) missing values Missing
rh has 289 (3.6%) missing values Missing
ah has 289 (3.6%) missing values Missing
date is uniformly distributed Uniform
date has unique values Unique

Reproduction

Analysis started2022-06-06 13:02:24.660320
Analysis finished2022-06-06 13:03:06.590200
Duration41.93 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7998
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
2004-03-10 18:00:00
 
1
2004-10-18 17:00:00
 
1
2004-10-19 06:00:00
 
1
2004-10-19 05:00:00
 
1
2004-10-19 04:00:00
 
1
Other values (7993)
7993 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7998 ?
Unique (%)100.0%

Sample

1st row2004-03-10 18:00:00
2nd row2004-03-10 19:00:00
3rd row2004-03-10 20:00:00
4th row2004-03-10 21:00:00
5th row2004-03-10 22:00:00

Common Values

ValueCountFrequency (%)
2004-03-10 18:00:001
 
< 0.1%
2004-10-18 17:00:001
 
< 0.1%
2004-10-19 06:00:001
 
< 0.1%
2004-10-19 05:00:001
 
< 0.1%
2004-10-19 04:00:001
 
< 0.1%
2004-10-19 03:00:001
 
< 0.1%
2004-10-19 02:00:001
 
< 0.1%
2004-10-19 01:00:001
 
< 0.1%
2004-10-19 00:00:001
 
< 0.1%
2004-10-18 23:00:001
 
< 0.1%
Other values (7988)7988
99.9%

Length

2022-06-06T15:03:06.704221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19:00:00334
 
2.1%
20:00:00334
 
2.1%
18:00:00334
 
2.1%
23:00:00334
 
2.1%
22:00:00334
 
2.1%
21:00:00334
 
2.1%
07:00:00333
 
2.1%
08:00:00333
 
2.1%
09:00:00333
 
2.1%
10:00:00333
 
2.1%
Other values (348)12660
79.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

co_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte CO-Konzentration

Distinct56
Distinct (%)0.9%
Missing1839
Missing (%)23.0%
Infinite0
Infinite (%)0.0%
Mean2.043870758
Minimum0.1
Maximum5.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:06.869006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.1
median1.8
Q32.8
95-th percentile4.5
Maximum5.6
Range5.5
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation1.230392574
Coefficient of variation (CV)0.6019913777
Kurtosis-0.1462069728
Mean2.043870758
Median Absolute Deviation (MAD)0.8
Skewness0.7347462252
Sum12588.2
Variance1.513865885
MonotonicityNot monotonic
2022-06-06T15:03:07.052845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1240
 
3.0%
1.6227
 
2.8%
1.7221
 
2.8%
1.4215
 
2.7%
1.5215
 
2.7%
0.7214
 
2.7%
1.3212
 
2.7%
1.2209
 
2.6%
1.1203
 
2.5%
0.8197
 
2.5%
Other values (46)4006
50.1%
(Missing)1839
23.0%
ValueCountFrequency (%)
0.125
 
0.3%
0.237
 
0.5%
0.387
 
1.1%
0.4134
1.7%
0.5181
2.3%
0.6197
2.5%
0.7214
2.7%
0.8197
2.5%
0.9192
2.4%
1240
3.0%
ValueCountFrequency (%)
5.623
0.3%
5.519
0.2%
5.414
0.2%
5.312
 
0.2%
5.218
0.2%
5.121
0.3%
534
0.4%
4.928
0.4%
4.833
0.4%
4.731
0.4%

pt08_s1_co
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Sensorreaktion (nominell auf CO ausgerichtet) (Zinnoxid)

Distinct949
Distinct (%)12.5%
Missing383
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean1089.757321
Minimum647
Maximum1683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:07.343376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile806
Q1930
median1058
Q31225
95-th percentile1484.3
Maximum1683
Range1036
Interquartile range (IQR)295

Descriptive statistics

Standard deviation207.1043801
Coefficient of variation (CV)0.1900463306
Kurtosis-0.3280884965
Mean1089.757321
Median Absolute Deviation (MAD)143
Skewness0.5584003534
Sum8298502
Variance42892.22426
MonotonicityNot monotonic
2022-06-06T15:03:07.663624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96925
 
0.3%
110024
 
0.3%
92524
 
0.3%
97324
 
0.3%
96622
 
0.3%
89222
 
0.3%
105022
 
0.3%
96222
 
0.3%
112421
 
0.3%
105321
 
0.3%
Other values (939)7388
92.4%
(Missing)383
 
4.8%
ValueCountFrequency (%)
6471
 
< 0.1%
6491
 
< 0.1%
6551
 
< 0.1%
6673
< 0.1%
6691
 
< 0.1%
6761
 
< 0.1%
6781
 
< 0.1%
6791
 
< 0.1%
6811
 
< 0.1%
6832
< 0.1%
ValueCountFrequency (%)
16831
 
< 0.1%
16811
 
< 0.1%
16801
 
< 0.1%
16781
 
< 0.1%
16771
 
< 0.1%
16763
< 0.1%
16743
< 0.1%
16731
 
< 0.1%
16692
< 0.1%
16672
< 0.1%

nmhc_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Gesamtkonzentration an nicht-metanischem Kohlenwasserstoff

Distinct379
Distinct (%)44.1%
Missing7139
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean183.2142026
Minimum7
Maximum642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:07.979054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile27
Q166
median137
Q3260.5
95-th percentile509.2
Maximum642
Range635
Interquartile range (IQR)194.5

Descriptive statistics

Standard deviation149.7677557
Coefficient of variation (CV)0.8174462109
Kurtosis0.4255311145
Mean183.2142026
Median Absolute Deviation (MAD)82
Skewness1.120716579
Sum157381
Variance22430.38064
MonotonicityNot monotonic
2022-06-06T15:03:08.219917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6614
 
0.2%
409
 
0.1%
299
 
0.1%
938
 
0.1%
888
 
0.1%
557
 
0.1%
847
 
0.1%
577
 
0.1%
607
 
0.1%
957
 
0.1%
Other values (369)776
 
9.7%
(Missing)7139
89.3%
ValueCountFrequency (%)
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
142
< 0.1%
161
 
< 0.1%
174
0.1%
182
< 0.1%
192
< 0.1%
ValueCountFrequency (%)
6421
< 0.1%
6391
< 0.1%
6351
< 0.1%
6331
< 0.1%
6241
< 0.1%
6181
< 0.1%
6091
< 0.1%
5991
< 0.1%
5971
< 0.1%
5921
< 0.1%

c6h6_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Benzolkonzentration

Distinct289
Distinct (%)3.9%
Missing492
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean9.795057288
Minimum0.1
Maximum28.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:08.421450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.8
Q14.6
median8.4
Q313.9
95-th percentile22.4
Maximum28.9
Range28.8
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.437831501
Coefficient of variation (CV)0.6572530728
Kurtosis-0.1028522452
Mean9.795057288
Median Absolute Deviation (MAD)4.4
Skewness0.7879044509
Sum73521.7
Variance41.44567443
MonotonicityNot monotonic
2022-06-06T15:03:08.604460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.668
 
0.9%
368
 
0.9%
467
 
0.8%
2.867
 
0.8%
3.865
 
0.8%
2.663
 
0.8%
662
 
0.8%
5.462
 
0.8%
3.161
 
0.8%
6.459
 
0.7%
Other values (279)6864
85.8%
(Missing)492
 
6.2%
ValueCountFrequency (%)
0.12
 
< 0.1%
0.25
 
0.1%
0.37
 
0.1%
0.413
0.2%
0.515
0.2%
0.617
0.2%
0.725
0.3%
0.818
0.2%
0.919
0.2%
117
0.2%
ValueCountFrequency (%)
28.94
0.1%
28.83
< 0.1%
28.72
 
< 0.1%
28.64
0.1%
28.52
 
< 0.1%
28.42
 
< 0.1%
28.34
0.1%
28.23
< 0.1%
28.12
 
< 0.1%
286
0.1%

pt08_s2_nmhc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Sensorreaktion (nominell auf NMHC ausgerichtet) (Titandioxid)

Distinct858
Distinct (%)15.0%
Missing2265
Missing (%)28.3%
Infinite0
Infinite (%)0.0%
Mean1051.347811
Minimum749
Maximum1701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:08.781716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum749
5-th percentile778
Q1880
median1014
Q31189
95-th percentile1451
Maximum1701
Range952
Interquartile range (IQR)309

Descriptive statistics

Standard deviation210.1267813
Coefficient of variation (CV)0.1998641925
Kurtosis-0.2005446695
Mean1051.347811
Median Absolute Deviation (MAD)149
Skewness0.6830040275
Sum6027377
Variance44153.26421
MonotonicityNot monotonic
2022-06-06T15:03:08.940745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85322
 
0.3%
85920
 
0.3%
77620
 
0.3%
76919
 
0.2%
81419
 
0.2%
88019
 
0.2%
80018
 
0.2%
89018
 
0.2%
96218
 
0.2%
90018
 
0.2%
Other values (848)5542
69.3%
(Missing)2265
28.3%
ValueCountFrequency (%)
7499
0.1%
75010
0.1%
75111
0.1%
75210
0.1%
75310
0.1%
75412
0.2%
7555
0.1%
7568
0.1%
7578
0.1%
7588
0.1%
ValueCountFrequency (%)
17012
< 0.1%
16992
< 0.1%
16971
< 0.1%
16961
< 0.1%
16922
< 0.1%
16901
< 0.1%
16891
< 0.1%
16881
< 0.1%
16862
< 0.1%
16851
< 0.1%

nox_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Echte stuendlich gemittelte NOx-Konzentration

Distinct609
Distinct (%)10.2%
Missing2032
Missing (%)25.4%
Infinite0
Infinite (%)0.0%
Mean193.8333892
Minimum2
Maximum625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:09.096151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile34
Q184.25
median152
Q3267
95-th percentile503.75
Maximum625
Range623
Interquartile range (IQR)182.75

Descriptive statistics

Standard deviation143.300945
Coefficient of variation (CV)0.7392995891
Kurtosis0.3956398222
Mean193.8333892
Median Absolute Deviation (MAD)83
Skewness1.065500588
Sum1156410
Variance20535.16084
MonotonicityNot monotonic
2022-06-06T15:03:09.250005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8939
 
0.5%
6537
 
0.5%
4136
 
0.5%
5732
 
0.4%
5132
 
0.4%
9331
 
0.4%
6131
 
0.4%
4631
 
0.4%
18031
 
0.4%
10430
 
0.4%
Other values (599)5636
70.5%
(Missing)2032
 
25.4%
ValueCountFrequency (%)
21
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
103
< 0.1%
114
0.1%
124
0.1%
134
0.1%
ValueCountFrequency (%)
6252
< 0.1%
6242
< 0.1%
6231
 
< 0.1%
6223
< 0.1%
6211
 
< 0.1%
6202
< 0.1%
6191
 
< 0.1%
6181
 
< 0.1%
6172
< 0.1%
6153
< 0.1%

pt08_s3_nox
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemitteltes Sensoransprechverhalten (nominell auf NOx ausgerichtet)

Distinct1035
Distinct (%)13.8%
Missing503
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean825.3547698
Minimum322
Maximum1452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:09.412663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile490
Q1667
median809
Q3966
95-th percentile1225
Maximum1452
Range1130
Interquartile range (IQR)299

Descriptive statistics

Standard deviation219.5969097
Coefficient of variation (CV)0.2660636585
Kurtosis-0.2633202158
Mean825.3547698
Median Absolute Deviation (MAD)149
Skewness0.3648026279
Sum6186034
Variance48222.80273
MonotonicityNot monotonic
2022-06-06T15:03:09.673071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84624
 
0.3%
73324
 
0.3%
76722
 
0.3%
81621
 
0.3%
80021
 
0.3%
68520
 
0.3%
87620
 
0.3%
76520
 
0.3%
76419
 
0.2%
82219
 
0.2%
Other values (1025)7285
91.1%
(Missing)503
 
6.3%
ValueCountFrequency (%)
3221
< 0.1%
3252
< 0.1%
3281
< 0.1%
3301
< 0.1%
3341
< 0.1%
3351
< 0.1%
3402
< 0.1%
3411
< 0.1%
3472
< 0.1%
3481
< 0.1%
ValueCountFrequency (%)
14522
< 0.1%
14513
< 0.1%
14502
< 0.1%
14492
< 0.1%
14481
 
< 0.1%
14471
 
< 0.1%
14451
 
< 0.1%
14421
 
< 0.1%
14411
 
< 0.1%
14401
 
< 0.1%

no2_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte NO2-Konzentration

Distinct215
Distinct (%)3.4%
Missing1694
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean104.8553299
Minimum2
Maximum220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:09.832432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile41
Q173
median103
Q3131
95-th percentile180
Maximum220
Range218
Interquartile range (IQR)58

Descriptive statistics

Standard deviation41.93701233
Coefficient of variation (CV)0.3999511742
Kurtosis-0.3530773838
Mean104.8553299
Median Absolute Deviation (MAD)29
Skewness0.3108419796
Sum661008
Variance1758.713003
MonotonicityNot monotonic
2022-06-06T15:03:09.980961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9772
 
0.9%
9569
 
0.9%
10167
 
0.8%
9666
 
0.8%
12165
 
0.8%
11465
 
0.8%
11964
 
0.8%
10764
 
0.8%
11064
 
0.8%
11263
 
0.8%
Other values (205)5645
70.6%
(Missing)1694
 
21.2%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
131
 
< 0.1%
145
0.1%
ValueCountFrequency (%)
2203
 
< 0.1%
2193
 
< 0.1%
2184
 
0.1%
2176
0.1%
2164
 
0.1%
2154
 
0.1%
2146
0.1%
2134
 
0.1%
21210
0.1%
2113
 
< 0.1%

pt08_s4_no2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Sensorreaktion (nominell auf NO2 ausgerichtet) (Wolframoxid)

Distinct1440
Distinct (%)19.0%
Missing414
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean1493.76635
Minimum702
Maximum2307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:10.127895image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum702
5-th percentile940.15
Q11303
median1503
Q31696.25
95-th percentile2008
Maximum2307
Range1605
Interquartile range (IQR)393.25

Descriptive statistics

Standard deviation310.6669619
Coefficient of variation (CV)0.207975606
Kurtosis-0.2016777694
Mean1493.76635
Median Absolute Deviation (MAD)197
Skewness-0.0790771727
Sum11328724
Variance96513.96123
MonotonicityNot monotonic
2022-06-06T15:03:10.278930image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158022
 
0.3%
153920
 
0.3%
148819
 
0.2%
146719
 
0.2%
163819
 
0.2%
141818
 
0.2%
157017
 
0.2%
151117
 
0.2%
148116
 
0.2%
132116
 
0.2%
Other values (1430)7401
92.5%
(Missing)414
 
5.2%
ValueCountFrequency (%)
7022
< 0.1%
7092
< 0.1%
7172
< 0.1%
7181
< 0.1%
7191
< 0.1%
7201
< 0.1%
7221
< 0.1%
7262
< 0.1%
7272
< 0.1%
7281
< 0.1%
ValueCountFrequency (%)
23071
< 0.1%
23061
< 0.1%
23052
< 0.1%
23012
< 0.1%
22991
< 0.1%
22981
< 0.1%
22891
< 0.1%
22882
< 0.1%
22871
< 0.1%
22831
< 0.1%

pt08_s5_o3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemitteltes Sensoransprechverhalten (nominell O3-bezogen) (Indiumoxid)

Distinct1604
Distinct (%)21.0%
Missing373
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean1011.02977
Minimum253
Maximum2073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:10.443723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum253
5-th percentile469
Q1735
median956
Q31256
95-th percentile1704.8
Maximum2073
Range1820
Interquartile range (IQR)521

Descriptive statistics

Standard deviation375.2464147
Coefficient of variation (CV)0.3711526858
Kurtosis-0.3161894148
Mean1011.02977
Median Absolute Deviation (MAD)252
Skewness0.5027411467
Sum7709102
Variance140809.8718
MonotonicityNot monotonic
2022-06-06T15:03:10.612369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83619
 
0.2%
82518
 
0.2%
77717
 
0.2%
82617
 
0.2%
79917
 
0.2%
73715
 
0.2%
92615
 
0.2%
71214
 
0.2%
59414
 
0.2%
85314
 
0.2%
Other values (1594)7465
93.3%
(Missing)373
 
4.7%
ValueCountFrequency (%)
2531
 
< 0.1%
2611
 
< 0.1%
2631
 
< 0.1%
2661
 
< 0.1%
2681
 
< 0.1%
2743
< 0.1%
2821
 
< 0.1%
2831
 
< 0.1%
2861
 
< 0.1%
2881
 
< 0.1%
ValueCountFrequency (%)
20731
 
< 0.1%
20691
 
< 0.1%
20681
 
< 0.1%
20661
 
< 0.1%
20621
 
< 0.1%
20591
 
< 0.1%
20581
 
< 0.1%
20561
 
< 0.1%
20541
 
< 0.1%
20513
< 0.1%

t
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Temperatur

Distinct417
Distinct (%)5.4%
Missing291
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean19.46956014
Minimum0.3
Maximum43.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:10.781152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile5.7
Q113.1
median19.3
Q325.4
95-th percentile35.2
Maximum43.4
Range43.1
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation8.6540469
Coefficient of variation (CV)0.4444911358
Kurtosis-0.4785626279
Mean19.46956014
Median Absolute Deviation (MAD)6.1
Skewness0.244441862
Sum150051.9
Variance74.89252775
MonotonicityNot monotonic
2022-06-06T15:03:10.939912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.855
 
0.7%
21.350
 
0.6%
20.247
 
0.6%
19.846
 
0.6%
23.743
 
0.5%
21.742
 
0.5%
15.642
 
0.5%
13.842
 
0.5%
14.641
 
0.5%
19.741
 
0.5%
Other values (407)7258
90.7%
(Missing)291
 
3.6%
ValueCountFrequency (%)
0.31
 
< 0.1%
0.61
 
< 0.1%
0.83
< 0.1%
13
< 0.1%
1.23
< 0.1%
1.34
0.1%
1.44
0.1%
1.52
< 0.1%
1.62
< 0.1%
1.71
 
< 0.1%
ValueCountFrequency (%)
43.41
 
< 0.1%
43.11
 
< 0.1%
42.83
< 0.1%
42.71
 
< 0.1%
42.61
 
< 0.1%
42.51
 
< 0.1%
42.22
< 0.1%
422
< 0.1%
41.92
< 0.1%
41.82
< 0.1%

rh
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Relative Luftfeuchtigkeit

Distinct748
Distinct (%)9.7%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean48.96924374
Minimum9.2
Maximum88.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:11.095498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile20.1
Q135.5
median49.4
Q362.1
95-th percentile77.3
Maximum88.7
Range79.5
Interquartile range (IQR)26.6

Descriptive statistics

Standard deviation17.28637789
Coefficient of variation (CV)0.3530047959
Kurtosis-0.8243365121
Mean48.96924374
Median Absolute Deviation (MAD)13.2
Skewness-0.0500365358
Sum377503.9
Variance298.8188606
MonotonicityNot monotonic
2022-06-06T15:03:11.251768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.129
 
0.4%
57.926
 
0.3%
50.825
 
0.3%
61.125
 
0.3%
60.825
 
0.3%
57.624
 
0.3%
50.924
 
0.3%
50.124
 
0.3%
42.823
 
0.3%
45.923
 
0.3%
Other values (738)7461
93.3%
(Missing)289
 
3.6%
ValueCountFrequency (%)
9.22
< 0.1%
9.31
< 0.1%
9.61
< 0.1%
9.81
< 0.1%
9.91
< 0.1%
102
< 0.1%
10.21
< 0.1%
10.71
< 0.1%
10.91
< 0.1%
11.61
< 0.1%
ValueCountFrequency (%)
88.71
 
< 0.1%
87.21
 
< 0.1%
87.11
 
< 0.1%
871
 
< 0.1%
86.61
 
< 0.1%
86.52
< 0.1%
861
 
< 0.1%
85.73
< 0.1%
85.61
 
< 0.1%
85.51
 
< 0.1%

ah
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Absolute Luftfeuchtigkeit

Distinct5917
Distinct (%)76.8%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1.082098625
Minimum0.1988
Maximum2.231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-06-06T15:03:11.416369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1988
5-th percentile0.4307
Q10.8167
median1.0468
Q31.3713
95-th percentile1.74482
Maximum2.231
Range2.0322
Interquartile range (IQR)0.5546

Descriptive statistics

Standard deviation0.3946499539
Coefficient of variation (CV)0.364707934
Kurtosis-0.5442091378
Mean1.082098625
Median Absolute Deviation (MAD)0.2751
Skewness0.163900465
Sum8341.8983
Variance0.1557485861
MonotonicityNot monotonic
2022-06-06T15:03:11.580833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83946
 
0.1%
1.11996
 
0.1%
0.87365
 
0.1%
0.92715
 
0.1%
0.83255
 
0.1%
0.96845
 
0.1%
1.05945
 
0.1%
0.89444
 
0.1%
1.05514
 
0.1%
1.16654
 
0.1%
Other values (5907)7660
95.8%
(Missing)289
 
3.6%
ValueCountFrequency (%)
0.19881
< 0.1%
0.20291
< 0.1%
0.2181
< 0.1%
0.21851
< 0.1%
0.21931
< 0.1%
0.23971
< 0.1%
0.2421
< 0.1%
0.24621
< 0.1%
0.24771
< 0.1%
0.25811
< 0.1%
ValueCountFrequency (%)
2.2311
< 0.1%
2.18061
< 0.1%
2.17661
< 0.1%
2.17191
< 0.1%
2.13951
< 0.1%
2.13621
< 0.1%
2.12471
< 0.1%
2.11951
< 0.1%
2.1171
< 0.1%
2.11641
< 0.1%

Interactions

2022-06-06T15:03:02.957255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:31.276438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:33.600158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:36.271212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:39.210722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:42.361332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:45.122374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:49.006322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:52.066327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:54.060266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:55.969629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:58.353778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:00.835703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:03.104789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:31.580638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:33.753955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:36.481206image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:39.470720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:42.604562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:45.294641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:49.275817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:52.232031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:54.205806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:56.106133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:58.616391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:00.990444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:03.259629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:31.775721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:33.923392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:36.680310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:39.676694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:42.808869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:45.473955image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:49.604860image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:52.397022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:54.347541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:56.239511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:58.824933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:01.140854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:03.424066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:31.969944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:34.112013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:36.887388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:39.936640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:43.008185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:45.791407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:49.917360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:52.569840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:54.506590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:56.388148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:59.060794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:01.294423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:03.574439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:32.139124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:34.273853image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:37.116421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:40.172192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:43.206271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:46.054192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:50.210944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:52.709929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:54.668509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:56.539373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:59.238871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:01.442495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:03:03.714724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:32.313251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:34.433076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:37.301327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:40.422042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:43.389446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:46.306666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:50.474951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:52.839100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:54.811300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:56.684554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-06-06T15:02:59.407646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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Correlations

2022-06-06T15:03:11.742740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-06T15:03:11.963634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-06T15:03:12.283611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-06T15:03:12.501777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-06T15:03:05.225487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-06T15:03:05.573905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-06T15:03:06.113421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-06T15:03:06.436878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

dateco_gtpt08_s1_conmhc_gtc6h6_gtpt08_s2_nmhcnox_gtpt08_s3_noxno2_gtpt08_s4_no2pt08_s5_o3trhah
02004-03-10 18:00:002.61360.0150.011.91046.0166.01056.0113.01692.01268.013.648.90.7578
12004-03-10 19:00:002.01292.0112.09.4955.0103.01174.092.01559.0972.013.347.70.7255
22004-03-10 20:00:002.21402.088.09.0939.0131.01140.0114.01555.01074.011.954.00.7502
32004-03-10 21:00:002.21376.080.09.2948.0172.01092.0122.01584.01203.011.060.00.7867
42004-03-10 22:00:001.61272.051.06.5836.0131.01205.0116.01490.01110.011.259.60.7888
52004-03-10 23:00:001.21197.038.04.7750.089.01337.096.01393.0949.011.259.20.7848
62004-03-11 00:00:001.21185.031.03.6NaN62.0NaN77.01333.0733.011.356.80.7603
72004-03-11 01:00:001.01136.031.03.3NaN62.0NaN76.01333.0730.010.760.00.7702
82004-03-11 02:00:000.91094.024.02.3NaN45.0NaN60.01276.0620.010.759.70.7648
92004-03-11 03:00:000.61010.019.01.7NaNNaNNaNNaN1235.0501.010.360.20.7517

Last rows

dateco_gtpt08_s1_conmhc_gtc6h6_gtpt08_s2_nmhcnox_gtpt08_s3_noxno2_gtpt08_s4_no2pt08_s5_o3trhah
79882005-02-06 14:00:001.0868.0NaN2.1NaN127.01081.0100.0753.0420.010.626.00.3320
79892005-02-06 15:00:000.8868.0NaN1.9NaN96.01128.078.0755.0363.010.327.70.3481
79902005-02-06 16:00:001.0904.0NaN2.7NaN138.01040.0100.0789.0410.010.228.30.3516
79912005-02-06 17:00:001.4944.0NaN3.7NaN217.0928.0150.0832.0568.09.229.90.3479
79922005-02-06 18:00:001.1925.0NaN2.9NaN186.01003.0142.0819.0570.06.936.40.3635
79932005-02-06 19:00:001.6985.0NaN4.5NaN227.0891.0165.0875.0774.06.038.00.3584
79942005-02-06 20:00:001.81002.0NaN5.3780.0252.0855.0179.0892.0857.05.836.40.3385
79952005-02-06 21:00:001.4938.0NaN3.7NaN193.0937.0149.0805.0737.05.835.40.3286
79962005-02-06 22:00:001.1896.0NaN2.6NaN158.01033.0126.0782.0610.05.436.60.3304
79972005-02-06 23:00:001.0907.0NaN2.4NaN150.01052.0120.0782.0627.05.137.90.3358